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Abstract

This trial has the identifier SPYLUN2012_MZ-Estacao Agraria do Umbeluzi. It was conducted under the supervision of x y as a Advanced Trial as part of a Yield Breeding Program in Estacao Agraria do Umbeluzi, Mozambique, Z in 2016. A total of 59 clones (including reference clones) were evaluated for 34 traits.

Materials and Methods

Location characterization

Installation

Geographic and climate characterization

Weather during planting season

Soil

Field management

Observations on special events

Materials

Trait descriptions (from ontology)

Model specification and data description

There is data from 59 treatments, evaluated using a randomize complete block design with 1, 2 blocks. The statistical model is \[ y_{ij} = \mu + \tau_i + \beta_j + \epsilon_{ij} \] where

  • \(y_{ij}\) is the observed response with treatment \(i\) and block \(j\).
  • \(\mu\) is the mean response over all treatments and blocks.
  • \(\tau_i\) is the effect for treatment \(i\).
  • \(\beta_j\) is the effect for block \(j\).
  • \(\epsilon_{ij}\) is the error term.

In this model we assume that the errors are independent and have a normal distribution with common variance, that is, \(\epsilon_{ij} \sim N(0,\sigma_{\epsilon}^2)\).

The following traits are analyzed: Beta carotene content measuring mg per 100g, Content of iron on dry weight basis measuring mg per 100g, Content of zinc on dry weight basis measuring mg per 100g, Dry weight of storage root samples measuring g of sample, Fibers in cooked samples 1 estimating 1-9, Fresh weight of storage root samples measuring g of sample, Fructose content measuring percent, Glucose content measuring percent, Harvest index computing percent, Number of commercial storage roots counting number per plot, Number of non-commercial storage roots counting number per plot, Overall taste of cooked sample 1 estimating 1-9, Plants established counting number per plot, Plants harvested counting number per plot, Plants planted counting number per plot, Plants with storage roots counting number per plot, Protein content measuring percent, Storage root damages estimatimg 1-9, Storage root dry matter content computing percent, Storage root form estimating 1-9, Storage root size estimating 1-9, Storage root starch content measuring percent, Storage root sweetness 1 estimating 1-9, Storage root texture 1 estimating 1-9, Sucrose content measuring percent, Survival index computing percent, Sweet potato weevil symptoms 1 estimating 1-9, Total carotenoids measuring mg per 100g, Vine vigor 1 estimating 1-9, Virus symptoms 1 estimating 1-9, Virus symptoms 2 estimating 1-9, Weight of commercial storage roots measuring kg per plot, Weight of non-commercial storage roots measuring kg per plot, Weight of vines measuring kg per plot.

The following germplasm was analyzed: MUSG11030-9, MUSG11049-5, MUSG11016-16, MUSG11001-2, MUSG11049-7, MUSG11046-18, MUSG11003-10, MUSG11016-2, MUSG11022-10, MUSG11026-11, Resisto, MUSG11010-11, MUSG11049-16, MUSG11048-15, MUSG11023-11, MUSG11022-11, MUSG11016-19, MUSG11021-16, MUSG11016-12, MUSG11004-9, MUSG11012-14, MUSG11016-21, Jonathan, MUSG11046-7, MUSG11036-3, MUSG11006-8, MUSG11033-6, MUSG11010-7, MUSG11044-15, MUSG11040-15, MUSG11050-3, MUSG11011-3, MUSG11016-14, MUSG11022-1, MUSG11002-9, MUSG11044-16, MUSG11046-3, MUSG11008-12, MUSG11040-16, MUSG11001-11, MUSG11042-7, MUSG11016-18, MUSG11046-14, MUSG11019-5, Chingova, MUSG11006-15, MUSG11010-19, MUSG11049-3, MUSG11019-15, MUSG11016-22, MUSG11004-5, MUSG11048-16, MUSG11040-13, MUSG11049-2, MUSG11007-15, MUSG11019-17, MUSG11007-1, MUSG11003-2, MUSG11016-10.

Computational tools

This report was created using x86_64-apple-darwin13.4.0, x86_64, darwin13.4.0, x86_64, darwin13.4.0, , 3, 2.3, 2015, 12, 10, 69752, R, R version 3.2.3 (2015-12-10), Wooden Christmas-Tree on a x86_64-apple-darwin13.4.0 (64-bit) running OS X 10.11.3 (El Capitan) in . The following base packages were loaded: stats, graphics, grDevices, utils, datasets, methods, base and the following additional packages: brapi, shinyURL, rmdformats, knitr, qtlcharts, d3heatmap, rhandsontable, dplyr, shinydashboard, ggplot2, leaflet, miniUI, shiny.

Results

Raw data

Trait summaries

Trait analyses

The following traits were not analyzed since they had too many missing values (>= 10%): Sucrose content measuring percent. For the remaining traits missing values were imputed using all available information.

Valid traits: Beta carotene content measuring mg per 100g, Content of iron on dry weight basis measuring mg per 100g, Content of zinc on dry weight basis measuring mg per 100g, Dry weight of storage root samples measuring g of sample, Fibers in cooked samples 1 estimating 1-9, Fresh weight of storage root samples measuring g of sample, Fructose content measuring percent, Glucose content measuring percent, Harvest index computing percent, Number of commercial storage roots counting number per plot, Number of non-commercial storage roots counting number per plot, Overall taste of cooked sample 1 estimating 1-9, Plants established counting number per plot, Plants harvested counting number per plot, Plants planted counting number per plot, Plants with storage roots counting number per plot, Protein content measuring percent, Storage root damages estimatimg 1-9, Storage root dry matter content computing percent, Storage root form estimating 1-9, Storage root size estimating 1-9, Storage root starch content measuring percent, Storage root sweetness 1 estimating 1-9, Storage root texture 1 estimating 1-9, Survival index computing percent, Sweet potato weevil symptoms 1 estimating 1-9, Total carotenoids measuring mg per 100g, Vine vigor 1 estimating 1-9, Virus symptoms 1 estimating 1-9, Virus symptoms 2 estimating 1-9, Weight of commercial storage roots measuring kg per plot, Weight of non-commercial storage roots measuring kg per plot, Weight of vines measuring kg per plot.

Analysis of Beta carotene content measuring mg per 100g

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 58286.1 1004.93 4.63222 1.3378e-08
REP 1 4.80281 4.80281 0.0221385 0.882235
Residuals 58 12582.7 216.944 NA NA

The p-value for treatments is 0.000000013378 which is significant at the 5% level.

The means of your treatments are:

germplasmName Beta carotene content measuring mg per 100g
Chingova 29.6
Jonathan 24
MUSG11001-11 25.5
MUSG11001-2 25.6
MUSG11002-9 25
MUSG11003-10 14.7
MUSG11003-2 22.1
MUSG11004-5 63.5
MUSG11004-9 20.1
MUSG11006-15 21.1
MUSG11006-8 35.9
MUSG11007-1 14
MUSG11007-15 42.1
MUSG11008-12 92.3
MUSG11010-11 30.7
MUSG11010-19 31.5
MUSG11010-7 26.8
MUSG11011-3 20.9
MUSG11012-14 16.3
MUSG11016-10 25.3
MUSG11016-12 27.4
MUSG11016-14 95
MUSG11016-16 26.8
MUSG11016-18 10.8
MUSG11016-19 44.4
MUSG11016-2 23
MUSG11016-21 34.4
MUSG11016-22 79
MUSG11019-15 31
MUSG11019-17 35.4
MUSG11019-5 33
MUSG11021-16 33.4
MUSG11022-1 16.6
MUSG11022-10 29.7
MUSG11022-11 24.6
MUSG11023-11 3.58
MUSG11026-11 15.7
MUSG11030-9 7.01
MUSG11033-6 18.4
MUSG11036-3 11
MUSG11040-13 11
MUSG11040-15 19.1
MUSG11040-16 28.2
MUSG11042-7 27.9
MUSG11044-15 35.1
MUSG11044-16 42.2
MUSG11046-14 40.3
MUSG11046-18 7.74
MUSG11046-3 42.2
MUSG11046-7 13.8
MUSG11048-15 36.7
MUSG11048-16 54.8
MUSG11049-16 25.6
MUSG11049-2 68.2
MUSG11049-3 39.6
MUSG11049-5 99.6
MUSG11049-7 101
MUSG11050-3 19.5
Resisto 28.6

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Content of iron on dry weight basis measuring mg per 100g

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 4.39166 0.0757182 3.29239 5.5482e-06
REP 1 0.0812434 0.0812434 3.53264 0.0651986
Residuals 58 1.33388 0.022998 NA NA

The p-value for treatments is 0.0000055482 which is significant at the 5% level.

The means of your treatments are:

germplasmName Content of iron on dry weight basis measuring mg per 100g
Chingova 1.4
Jonathan 1.4
MUSG11001-11 1.48
MUSG11001-2 1.62
MUSG11002-9 1.48
MUSG11003-10 1.5
MUSG11003-2 1.47
MUSG11004-5 1.72
MUSG11004-9 1.47
MUSG11006-15 1.36
MUSG11006-8 1.82
MUSG11007-1 1.31
MUSG11007-15 1.82
MUSG11008-12 1.44
MUSG11010-11 1.74
MUSG11010-19 1.73
MUSG11010-7 1.5
MUSG11011-3 1.2
MUSG11012-14 1.41
MUSG11016-10 1.46
MUSG11016-12 1.54
MUSG11016-14 1.43
MUSG11016-16 1.89
MUSG11016-18 1.17
MUSG11016-19 1.71
MUSG11016-2 1.64
MUSG11016-21 1.75
MUSG11016-22 1.54
MUSG11019-15 1.62
MUSG11019-17 1.33
MUSG11019-5 1.52
MUSG11021-16 1.46
MUSG11022-1 1.53
MUSG11022-10 1.61
MUSG11022-11 1.55
MUSG11023-11 1.43
MUSG11026-11 1.34
MUSG11030-9 1.24
MUSG11033-6 1.45
MUSG11036-3 1.51
MUSG11040-13 1.35
MUSG11040-15 1.28
MUSG11040-16 1.47
MUSG11042-7 1.55
MUSG11044-15 1.73
MUSG11044-16 1.79
MUSG11046-14 1.7
MUSG11046-18 1.48
MUSG11046-3 1.82
MUSG11046-7 1.49
MUSG11048-15 1.89
MUSG11048-16 1.54
MUSG11049-16 1.79
MUSG11049-2 0.975
MUSG11049-3 1.41
MUSG11049-5 1.48
MUSG11049-7 1.03
MUSG11050-3 1.54
Resisto 1.47

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Content of zinc on dry weight basis measuring mg per 100g

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 2.20948 0.0380944 2.59694 0.000190213
REP 1 0.0707235 0.0707235 4.8213 0.0321237
Residuals 58 0.8508 0.014669 NA NA

The p-value for treatments is 0.000190213 which is significant at the 5% level.

The means of your treatments are:

germplasmName Content of zinc on dry weight basis measuring mg per 100g
Chingova 0.825
Jonathan 0.777
MUSG11001-11 0.795
MUSG11001-2 0.888
MUSG11002-9 0.905
MUSG11003-10 0.965
MUSG11003-2 0.817
MUSG11004-5 1.03
MUSG11004-9 0.889
MUSG11006-15 0.5
MUSG11006-8 1.1
MUSG11007-1 0.775
MUSG11007-15 1.03
MUSG11008-12 0.57
MUSG11010-11 0.93
MUSG11010-19 0.925
MUSG11010-7 0.944
MUSG11011-3 0.78
MUSG11012-14 0.829
MUSG11016-10 0.925
MUSG11016-12 1
MUSG11016-14 0.9
MUSG11016-16 1.23
MUSG11016-18 0.82
MUSG11016-19 0.98
MUSG11016-2 0.92
MUSG11016-21 1.03
MUSG11016-22 0.927
MUSG11019-15 0.974
MUSG11019-17 0.73
MUSG11019-5 0.925
MUSG11021-16 0.88
MUSG11022-1 0.93
MUSG11022-10 0.71
MUSG11022-11 0.84
MUSG11023-11 0.925
MUSG11026-11 0.865
MUSG11030-9 0.74
MUSG11033-6 0.845
MUSG11036-3 0.84
MUSG11040-13 0.67
MUSG11040-15 0.835
MUSG11040-16 0.775
MUSG11042-7 0.925
MUSG11044-15 1.09
MUSG11044-16 0.975
MUSG11046-14 0.975
MUSG11046-18 0.9
MUSG11046-3 1.01
MUSG11046-7 0.905
MUSG11048-15 0.875
MUSG11048-16 0.77
MUSG11049-16 1.17
MUSG11049-2 0.665
MUSG11049-3 0.72
MUSG11049-5 0.7
MUSG11049-7 0.595
MUSG11050-3 0.745
Resisto 0.84

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Dry weight of storage root samples measuring g of sample

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 432.934 7.46438 5.85059 1.24919e-10
REP 1 0.340422 0.340422 0.266823 0.607435
Residuals 58 73.9983 1.27583 NA NA

The p-value for treatments is 0.000000000124919 which is significant at the 5% level.

The means of your treatments are:

germplasmName Dry weight of storage root samples measuring g of sample
Chingova 16.7
Jonathan 11.8
MUSG11001-11 11.5
MUSG11001-2 11.9
MUSG11002-9 13.4
MUSG11003-10 13.1
MUSG11003-2 12
MUSG11004-5 10.8
MUSG11004-9 14.9
MUSG11006-15 11.7
MUSG11006-8 12.1
MUSG11007-1 11.9
MUSG11007-15 10.2
MUSG11008-12 13.2
MUSG11010-11 8.88
MUSG11010-19 11.9
MUSG11010-7 12.2
MUSG11011-3 15.5
MUSG11012-14 13.8
MUSG11016-10 15.8
MUSG11016-12 16.3
MUSG11016-14 15.1
MUSG11016-16 12.1
MUSG11016-18 14.4
MUSG11016-19 11.4
MUSG11016-2 15.5
MUSG11016-21 15
MUSG11016-22 14.4
MUSG11019-15 11.8
MUSG11019-17 14.1
MUSG11019-5 15.4
MUSG11021-16 11.2
MUSG11022-1 13.3
MUSG11022-10 11.6
MUSG11022-11 11.3
MUSG11023-11 14.2
MUSG11026-11 16.3
MUSG11030-9 16.1
MUSG11033-6 12.7
MUSG11036-3 8.83
MUSG11040-13 13.1
MUSG11040-15 13
MUSG11040-16 13.1
MUSG11042-7 12.7
MUSG11044-15 12.2
MUSG11044-16 12.7
MUSG11046-14 11.9
MUSG11046-18 12.5
MUSG11046-3 9.76
MUSG11046-7 12.6
MUSG11048-15 8.58
MUSG11048-16 12.1
MUSG11049-16 15.4
MUSG11049-2 14.7
MUSG11049-3 10.3
MUSG11049-5 12.5
MUSG11049-7 10.7
MUSG11050-3 13.8
Resisto 12.8

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Fibers in cooked samples 1 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 45.0508 0.776739 1.33702 0.135817
REP 1 0.305085 0.305085 0.525151 0.471566
Residuals 58 33.6949 0.580947 NA NA

The means of your treatments are:

germplasmName Fibers in cooked samples 1 estimating 1-9
Chingova 2
Jonathan 1.5
MUSG11001-11 2
MUSG11001-2 2
MUSG11002-9 2
MUSG11003-10 2
MUSG11003-2 2
MUSG11004-5 2
MUSG11004-9 2
MUSG11006-15 2
MUSG11006-8 2
MUSG11007-1 4
MUSG11007-15 2
MUSG11008-12 3.5
MUSG11010-11 2
MUSG11010-19 2
MUSG11010-7 2
MUSG11011-3 3
MUSG11012-14 2
MUSG11016-10 3
MUSG11016-12 3
MUSG11016-14 3
MUSG11016-16 4
MUSG11016-18 4
MUSG11016-19 2
MUSG11016-2 3
MUSG11016-21 2.5
MUSG11016-22 3
MUSG11019-15 2
MUSG11019-17 2
MUSG11019-5 2
MUSG11021-16 2
MUSG11022-1 2
MUSG11022-10 2
MUSG11022-11 3
MUSG11023-11 2
MUSG11026-11 3
MUSG11030-9 3
MUSG11033-6 3
MUSG11036-3 3
MUSG11040-13 2
MUSG11040-15 2
MUSG11040-16 2
MUSG11042-7 2
MUSG11044-15 2
MUSG11044-16 2
MUSG11046-14 2
MUSG11046-18 2.5
MUSG11046-3 2
MUSG11046-7 3
MUSG11048-15 2
MUSG11048-16 2
MUSG11049-16 3
MUSG11049-2 2
MUSG11049-3 2
MUSG11049-5 2
MUSG11049-7 3
MUSG11050-3 2
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Fresh weight of storage root samples measuring g of sample

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 370.073 6.38058 1.4633 0.075061
REP 1 4.97156 4.97156 1.14016 0.290043
Residuals 58 252.903 4.3604 NA NA

The means of your treatments are:

germplasmName Fresh weight of storage root samples measuring g of sample
Chingova 50.8
Jonathan 50.3
MUSG11001-11 50.8
MUSG11001-2 50.4
MUSG11002-9 50.1
MUSG11003-10 50.8
MUSG11003-2 49.9
MUSG11004-5 48.5
MUSG11004-9 50
MUSG11006-15 50.6
MUSG11006-8 50
MUSG11007-1 50.4
MUSG11007-15 50.5
MUSG11008-12 50.4
MUSG11010-11 50.4
MUSG11010-19 50.3
MUSG11010-7 50.2
MUSG11011-3 50.4
MUSG11012-14 50
MUSG11016-10 50.4
MUSG11016-12 50.6
MUSG11016-14 50.7
MUSG11016-16 42
MUSG11016-18 48.8
MUSG11016-19 50.3
MUSG11016-2 50.7
MUSG11016-21 50.3
MUSG11016-22 50.5
MUSG11019-15 50.4
MUSG11019-17 50.5
MUSG11019-5 50.4
MUSG11021-16 50.5
MUSG11022-1 50.5
MUSG11022-10 50.2
MUSG11022-11 46
MUSG11023-11 50.4
MUSG11026-11 50.5
MUSG11030-9 50.5
MUSG11033-6 50.7
MUSG11036-3 41.6
MUSG11040-13 50.4
MUSG11040-15 49.7
MUSG11040-16 50.3
MUSG11042-7 50.2
MUSG11044-15 50.8
MUSG11044-16 50.6
MUSG11046-14 50.4
MUSG11046-18 50.6
MUSG11046-3 50.8
MUSG11046-7 50.1
MUSG11048-15 46.3
MUSG11048-16 47.6
MUSG11049-16 50.3
MUSG11049-2 50.3
MUSG11049-3 50.9
MUSG11049-5 50.9
MUSG11049-7 50.4
MUSG11050-3 50.3
Resisto 49.2

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Fructose content measuring percent

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 913.264 15.7459 4.34654 4.4451e-08
REP 1 13.6037 13.6037 3.75519 0.0575172
Residuals 58 210.113 3.62264 NA NA

The p-value for treatments is 0.000000044451 which is significant at the 5% level.

The means of your treatments are:

germplasmName Fructose content measuring percent
Chingova 5.6
Jonathan 10.1
MUSG11001-11 10.5
MUSG11001-2 9.75
MUSG11002-9 6.75
MUSG11003-10 5.85
MUSG11003-2 8.54
MUSG11004-5 7.8
MUSG11004-9 5.47
MUSG11006-15 13.4
MUSG11006-8 8.44
MUSG11007-1 7.85
MUSG11007-15 9.4
MUSG11008-12 13.2
MUSG11010-11 17.1
MUSG11010-19 12.1
MUSG11010-7 5.96
MUSG11011-3 6.75
MUSG11012-14 8.34
MUSG11016-10 4.35
MUSG11016-12 4.8
MUSG11016-14 5.5
MUSG11016-16 5.3
MUSG11016-18 7.45
MUSG11016-19 6.8
MUSG11016-2 8.65
MUSG11016-21 5.15
MUSG11016-22 6.34
MUSG11019-15 8.78
MUSG11019-17 6.75
MUSG11019-5 8.75
MUSG11021-16 10.7
MUSG11022-1 9.5
MUSG11022-10 14.7
MUSG11022-11 9.65
MUSG11023-11 6.5
MUSG11026-11 5
MUSG11030-9 7.4
MUSG11033-6 11
MUSG11036-3 10.4
MUSG11040-13 9.5
MUSG11040-15 7.37
MUSG11040-16 7.7
MUSG11042-7 8.25
MUSG11044-15 7.65
MUSG11044-16 7.45
MUSG11046-14 8.3
MUSG11046-18 6.8
MUSG11046-3 11.1
MUSG11046-7 5.85
MUSG11048-15 14.9
MUSG11048-16 10.7
MUSG11049-16 3.95
MUSG11049-2 7.4
MUSG11049-3 10.6
MUSG11049-5 13.2
MUSG11049-7 12.3
MUSG11050-3 9.55
Resisto 8.52

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Glucose content measuring percent

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 1629.94 28.1024 4.23951 7.04929e-08
REP 1 25.3767 25.3767 3.82832 0.0552136
Residuals 58 384.464 6.62868 NA NA

The p-value for treatments is 0.0000000704929 which is significant at the 5% level.

The means of your treatments are:

germplasmName Glucose content measuring percent
Chingova 8.15
Jonathan 13.8
MUSG11001-11 14.8
MUSG11001-2 13.1
MUSG11002-9 9.35
MUSG11003-10 8
MUSG11003-2 11.5
MUSG11004-5 11
MUSG11004-9 7.77
MUSG11006-15 17.9
MUSG11006-8 12
MUSG11007-1 10.4
MUSG11007-15 12.8
MUSG11008-12 17.9
MUSG11010-11 23.4
MUSG11010-19 16.6
MUSG11010-7 8.35
MUSG11011-3 9.35
MUSG11012-14 11.6
MUSG11016-10 6.1
MUSG11016-12 6.95
MUSG11016-14 7.5
MUSG11016-16 8.25
MUSG11016-18 10.4
MUSG11016-19 9.3
MUSG11016-2 12.4
MUSG11016-21 7.3
MUSG11016-22 8.94
MUSG11019-15 12
MUSG11019-17 9.3
MUSG11019-5 11.9
MUSG11021-16 15.1
MUSG11022-1 13.3
MUSG11022-10 20.2
MUSG11022-11 13.2
MUSG11023-11 9.4
MUSG11026-11 7.15
MUSG11030-9 10.4
MUSG11033-6 15.2
MUSG11036-3 13.7
MUSG11040-13 12.9
MUSG11040-15 10.1
MUSG11040-16 10.7
MUSG11042-7 11.1
MUSG11044-15 10.8
MUSG11044-16 10.2
MUSG11046-14 11.7
MUSG11046-18 9.2
MUSG11046-3 15.3
MUSG11046-7 8.3
MUSG11048-15 20.4
MUSG11048-16 14.2
MUSG11049-16 5.75
MUSG11049-2 10.1
MUSG11049-3 14.2
MUSG11049-5 18.4
MUSG11049-7 16.8
MUSG11050-3 13.1
Resisto 11.7

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Harvest index computing percent

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 25796.9 444.774 2.93324 3.33194e-05
REP 1 183.467 183.467 1.20995 0.275887
Residuals 58 8794.68 151.632 NA NA

The p-value for treatments is 0.0000333194 which is significant at the 5% level.

The means of your treatments are:

germplasmName Harvest index computing percent
Chingova 40
Jonathan 30.4
MUSG11001-11 44.8
MUSG11001-2 90.8
MUSG11002-9 68.8
MUSG11003-10 52.1
MUSG11003-2 73.3
MUSG11004-5 60.3
MUSG11004-9 66.5
MUSG11006-15 62.4
MUSG11006-8 19.2
MUSG11007-1 63
MUSG11007-15 55
MUSG11008-12 61
MUSG11010-11 42.8
MUSG11010-19 44.5
MUSG11010-7 46.1
MUSG11011-3 40.7
MUSG11012-14 48.8
MUSG11016-10 47
MUSG11016-12 49.5
MUSG11016-14 42.9
MUSG11016-16 66.6
MUSG11016-18 50
MUSG11016-19 34.5
MUSG11016-2 68.8
MUSG11016-21 82.3
MUSG11016-22 84.8
MUSG11019-15 38.5
MUSG11019-17 33.8
MUSG11019-5 37.9
MUSG11021-16 57.3
MUSG11022-1 71.2
MUSG11022-10 79.7
MUSG11022-11 38.3
MUSG11023-11 52.7
MUSG11026-11 67.1
MUSG11030-9 56
MUSG11033-6 64.2
MUSG11036-3 55.5
MUSG11040-13 40.8
MUSG11040-15 24.4
MUSG11040-16 51
MUSG11042-7 42.5
MUSG11044-15 31.4
MUSG11044-16 44
MUSG11046-14 30.4
MUSG11046-18 64.5
MUSG11046-3 59
MUSG11046-7 52.9
MUSG11048-15 40.5
MUSG11048-16 45.5
MUSG11049-16 38.5
MUSG11049-2 54.3
MUSG11049-3 57.8
MUSG11049-5 53.8
MUSG11049-7 51.3
MUSG11050-3 61.3
Resisto 55

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Number of commercial storage roots counting number per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 38399.3 662.056 3.35825 4.02634e-06
REP 1 7601.43 7601.43 38.5579 6.13703e-08
Residuals 58 11434.3 197.143 NA NA

The p-value for treatments is 0.00000402634 which is significant at the 5% level.

The means of your treatments are:

germplasmName Number of commercial storage roots counting number per plot
Chingova 6.5
Jonathan 27.7
MUSG11001-11 26
MUSG11001-2 23.5
MUSG11002-9 39.5
MUSG11003-10 56
MUSG11003-2 20
MUSG11004-5 102
MUSG11004-9 5.5
MUSG11006-15 26
MUSG11006-8 4
MUSG11007-1 8
MUSG11007-15 38
MUSG11008-12 16.5
MUSG11010-11 34
MUSG11010-19 51.5
MUSG11010-7 15.5
MUSG11011-3 21
MUSG11012-14 15.5
MUSG11016-10 32.5
MUSG11016-12 60.5
MUSG11016-14 43
MUSG11016-16 40
MUSG11016-18 48.5
MUSG11016-19 49.5
MUSG11016-2 53.5
MUSG11016-21 35.5
MUSG11016-22 37
MUSG11019-15 24
MUSG11019-17 25
MUSG11019-5 14
MUSG11021-16 37
MUSG11022-1 35.5
MUSG11022-10 41.5
MUSG11022-11 37.5
MUSG11023-11 63.5
MUSG11026-11 24
MUSG11030-9 26
MUSG11033-6 41
MUSG11036-3 35.5
MUSG11040-13 35
MUSG11040-15 25.5
MUSG11040-16 47.5
MUSG11042-7 12.5
MUSG11044-15 39
MUSG11044-16 10.5
MUSG11046-14 28
MUSG11046-18 39.5
MUSG11046-3 49
MUSG11046-7 70.5
MUSG11048-15 24.5
MUSG11048-16 18
MUSG11049-16 22.5
MUSG11049-2 39.5
MUSG11049-3 61
MUSG11049-5 46
MUSG11049-7 64
MUSG11050-3 53
Resisto 28.4

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Number of non-commercial storage roots counting number per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 36365.3 626.989 2.73342 9.31683e-05
REP 1 1995.91 1995.91 8.70138 0.00457905
Residuals 58 13304 229.379 NA NA

The p-value for treatments is 0.0000931683 which is significant at the 5% level.

The means of your treatments are:

germplasmName Number of non-commercial storage roots counting number per plot
Chingova 6
Jonathan 13.5
MUSG11001-11 61.5
MUSG11001-2 24.5
MUSG11002-9 24
MUSG11003-10 22
MUSG11003-2 14
MUSG11004-5 67.5
MUSG11004-9 12
MUSG11006-15 41.5
MUSG11006-8 23.5
MUSG11007-1 17
MUSG11007-15 23.5
MUSG11008-12 15
MUSG11010-11 28
MUSG11010-19 57.5
MUSG11010-7 31.5
MUSG11011-3 23.5
MUSG11012-14 25
MUSG11016-10 13.5
MUSG11016-12 54
MUSG11016-14 19
MUSG11016-16 51
MUSG11016-18 28
MUSG11016-19 30
MUSG11016-2 45.5
MUSG11016-21 32.5
MUSG11016-22 33.5
MUSG11019-15 15.5
MUSG11019-17 46
MUSG11019-5 10
MUSG11021-16 46
MUSG11022-1 15
MUSG11022-10 57.5
MUSG11022-11 28.5
MUSG11023-11 76
MUSG11026-11 17.5
MUSG11030-9 28.5
MUSG11033-6 49
MUSG11036-3 24.5
MUSG11040-13 29
MUSG11040-15 20
MUSG11040-16 32
MUSG11042-7 9
MUSG11044-15 29.3
MUSG11044-16 15.5
MUSG11046-14 17
MUSG11046-18 28.5
MUSG11046-3 66.5
MUSG11046-7 69
MUSG11048-15 60
MUSG11048-16 23
MUSG11049-16 27.5
MUSG11049-2 15.3
MUSG11049-3 35.5
MUSG11049-5 61.5
MUSG11049-7 44
MUSG11050-3 51
Resisto 22.6

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Overall taste of cooked sample 1 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 164.441 2.83518 2.37445 0.000619079
REP 1 2.74576 2.74576 2.29956 0.134842
Residuals 58 69.2542 1.19404 NA NA

The p-value for treatments is 0.000619079 which is significant at the 5% level.

The means of your treatments are:

germplasmName Overall taste of cooked sample 1 estimating 1-9
Chingova 2
Jonathan 1.5
MUSG11001-11 2
MUSG11001-2 5
MUSG11002-9 4
MUSG11003-10 5
MUSG11003-2 2.5
MUSG11004-5 2.5
MUSG11004-9 3.5
MUSG11006-15 2.5
MUSG11006-8 2
MUSG11007-1 5.5
MUSG11007-15 3.5
MUSG11008-12 4
MUSG11010-11 2
MUSG11010-19 3.5
MUSG11010-7 2.5
MUSG11011-3 2
MUSG11012-14 2
MUSG11016-10 4
MUSG11016-12 5
MUSG11016-14 5
MUSG11016-16 5
MUSG11016-18 4
MUSG11016-19 5.5
MUSG11016-2 4.5
MUSG11016-21 5
MUSG11016-22 3.5
MUSG11019-15 2
MUSG11019-17 3.5
MUSG11019-5 4.5
MUSG11021-16 2.5
MUSG11022-1 3.5
MUSG11022-10 2
MUSG11022-11 3
MUSG11023-11 5
MUSG11026-11 4.5
MUSG11030-9 3
MUSG11033-6 2.5
MUSG11036-3 3.5
MUSG11040-13 2
MUSG11040-15 4
MUSG11040-16 3.5
MUSG11042-7 2.5
MUSG11044-15 2
MUSG11044-16 2
MUSG11046-14 4
MUSG11046-18 5
MUSG11046-3 2
MUSG11046-7 4
MUSG11048-15 2
MUSG11048-16 2
MUSG11049-16 3.5
MUSG11049-2 3.5
MUSG11049-3 4
MUSG11049-5 2.5
MUSG11049-7 5
MUSG11050-3 4
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Plants established counting number per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 1404.08 24.2084 2.02501 0.00404478
REP 1 7.12712 7.12712 0.596177 0.443175
Residuals 58 693.373 11.9547 NA NA

The p-value for treatments is 0.00404478 which is significant at the 5% level.

The means of your treatments are:

germplasmName Plants established counting number per plot
Chingova 20
Jonathan 14
MUSG11001-11 16
MUSG11001-2 16
MUSG11002-9 15.5
MUSG11003-10 21
MUSG11003-2 13
MUSG11004-5 22.5
MUSG11004-9 20
MUSG11006-15 22.5
MUSG11006-8 17
MUSG11007-1 14
MUSG11007-15 17.5
MUSG11008-12 20.5
MUSG11010-11 21
MUSG11010-19 18.5
MUSG11010-7 18.5
MUSG11011-3 20
MUSG11012-14 19.5
MUSG11016-10 19.5
MUSG11016-12 20.5
MUSG11016-14 21
MUSG11016-16 22.5
MUSG11016-18 20
MUSG11016-19 18.5
MUSG11016-2 23
MUSG11016-21 16.5
MUSG11016-22 14.5
MUSG11019-15 17.5
MUSG11019-17 21.5
MUSG11019-5 15.5
MUSG11021-16 18
MUSG11022-1 14.5
MUSG11022-10 21
MUSG11022-11 22
MUSG11023-11 18
MUSG11026-11 21.5
MUSG11030-9 15.5
MUSG11033-6 20
MUSG11036-3 16
MUSG11040-13 21.5
MUSG11040-15 14.5
MUSG11040-16 22.5
MUSG11042-7 10.5
MUSG11044-15 20
MUSG11044-16 12
MUSG11046-14 17.5
MUSG11046-18 16
MUSG11046-3 17
MUSG11046-7 21
MUSG11048-15 18
MUSG11048-16 13
MUSG11049-16 19.5
MUSG11049-2 16.5
MUSG11049-3 18
MUSG11049-5 19.5
MUSG11049-7 16.5
MUSG11050-3 17
Resisto 4

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Plants harvested counting number per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 1504.76 25.9442 2.36941 0.00063598
REP 1 125.778 125.778 11.487 0.00126576
Residuals 58 635.079 10.9496 NA NA

The p-value for treatments is 0.00063598 which is significant at the 5% level.

The means of your treatments are:

germplasmName Plants harvested counting number per plot
Chingova 4.5
Jonathan 4.97
MUSG11001-11 11
MUSG11001-2 10.5
MUSG11002-9 9.5
MUSG11003-10 15.5
MUSG11003-2 6.5
MUSG11004-5 21.5
MUSG11004-9 7.5
MUSG11006-15 11
MUSG11006-8 9.5
MUSG11007-1 8
MUSG11007-15 11.5
MUSG11008-12 9
MUSG11010-11 10.5
MUSG11010-19 17
MUSG11010-7 8
MUSG11011-3 12
MUSG11012-14 10.5
MUSG11016-10 10.5
MUSG11016-12 15
MUSG11016-14 10
MUSG11016-16 15.5
MUSG11016-18 13.5
MUSG11016-19 11.5
MUSG11016-2 13.5
MUSG11016-21 12.5
MUSG11016-22 9
MUSG11019-15 11.5
MUSG11019-17 13
MUSG11019-5 10.5
MUSG11021-16 13
MUSG11022-1 10
MUSG11022-10 14.5
MUSG11022-11 13.5
MUSG11023-11 20
MUSG11026-11 10
MUSG11030-9 10
MUSG11033-6 14.5
MUSG11036-3 9
MUSG11040-13 15
MUSG11040-15 9.5
MUSG11040-16 14.5
MUSG11042-7 6.5
MUSG11044-15 11.5
MUSG11044-16 4.5
MUSG11046-14 11
MUSG11046-18 13
MUSG11046-3 17.5
MUSG11046-7 14.5
MUSG11048-15 11
MUSG11048-16 7
MUSG11049-16 13.5
MUSG11049-2 12
MUSG11049-3 14.5
MUSG11049-5 17.5
MUSG11049-7 12
MUSG11050-3 15
Resisto 4.94

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Plants planted counting number per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 1.83016e-26 3.15544e-28 1 0.5
REP 1 3.15544e-28 3.15544e-28 1 0.321464
Residuals 58 1.83016e-26 3.15544e-28 NA NA

The means of your treatments are:

germplasmName Plants planted counting number per plot
Chingova 26
Jonathan 26
MUSG11001-11 26
MUSG11001-2 26
MUSG11002-9 26
MUSG11003-10 26
MUSG11003-2 26
MUSG11004-5 26
MUSG11004-9 26
MUSG11006-15 26
MUSG11006-8 26
MUSG11007-1 26
MUSG11007-15 26
MUSG11008-12 26
MUSG11010-11 26
MUSG11010-19 26
MUSG11010-7 26
MUSG11011-3 26
MUSG11012-14 26
MUSG11016-10 26
MUSG11016-12 26
MUSG11016-14 26
MUSG11016-16 26
MUSG11016-18 26
MUSG11016-19 26
MUSG11016-2 26
MUSG11016-21 26
MUSG11016-22 26
MUSG11019-15 26
MUSG11019-17 26
MUSG11019-5 26
MUSG11021-16 26
MUSG11022-1 26
MUSG11022-10 26
MUSG11022-11 26
MUSG11023-11 26
MUSG11026-11 26
MUSG11030-9 26
MUSG11033-6 26
MUSG11036-3 26
MUSG11040-13 26
MUSG11040-15 26
MUSG11040-16 26
MUSG11042-7 26
MUSG11044-15 26
MUSG11044-16 26
MUSG11046-14 26
MUSG11046-18 26
MUSG11046-3 26
MUSG11046-7 26
MUSG11048-15 26
MUSG11048-16 26
MUSG11049-16 26
MUSG11049-2 26
MUSG11049-3 26
MUSG11049-5 26
MUSG11049-7 26
MUSG11050-3 26
Resisto 26

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

## Warning in plot.window(...): relative range of values = 17 * EPS, is small
## (axis 1)

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Plants with storage roots counting number per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 1456.14 25.1058 2.36115 0.000664692
REP 1 138.265 138.265 13.0035 0.000647703
Residuals 58 616.707 10.6329 NA NA

The p-value for treatments is 0.000664692 which is significant at the 5% level.

The means of your treatments are:

germplasmName Plants with storage roots counting number per plot
Chingova 4
Jonathan 6.23
MUSG11001-11 11
MUSG11001-2 10
MUSG11002-9 9.5
MUSG11003-10 14.5
MUSG11003-2 6.5
MUSG11004-5 21.5
MUSG11004-9 7.5
MUSG11006-15 11
MUSG11006-8 6
MUSG11007-1 8
MUSG11007-15 11.5
MUSG11008-12 9
MUSG11010-11 10.5
MUSG11010-19 17
MUSG11010-7 8
MUSG11011-3 12
MUSG11012-14 10.5
MUSG11016-10 10
MUSG11016-12 14.5
MUSG11016-14 9.5
MUSG11016-16 15.5
MUSG11016-18 13.5
MUSG11016-19 11
MUSG11016-2 13.5
MUSG11016-21 12
MUSG11016-22 9
MUSG11019-15 11.5
MUSG11019-17 13
MUSG11019-5 9.5
MUSG11021-16 13
MUSG11022-1 10
MUSG11022-10 14.5
MUSG11022-11 13
MUSG11023-11 20
MUSG11026-11 10
MUSG11030-9 10
MUSG11033-6 14.5
MUSG11036-3 9
MUSG11040-13 15
MUSG11040-15 7
MUSG11040-16 14.5
MUSG11042-7 6.5
MUSG11044-15 11.5
MUSG11044-16 4.5
MUSG11046-14 11
MUSG11046-18 13
MUSG11046-3 17.5
MUSG11046-7 13
MUSG11048-15 11
MUSG11048-16 7
MUSG11049-16 13
MUSG11049-2 12
MUSG11049-3 14.5
MUSG11049-5 17
MUSG11049-7 12
MUSG11050-3 15
Resisto 9.77

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Protein content measuring percent

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 32.666 0.563207 1.88104 0.00874677
REP 1 1.39126 1.39126 4.64663 0.035276
Residuals 58 17.3659 0.299412 NA NA

The p-value for treatments is 0.00874677 which is significant at the 5% level.

The means of your treatments are:

germplasmName Protein content measuring percent
Chingova 3.2
Jonathan 2.49
MUSG11001-11 2.35
MUSG11001-2 2.7
MUSG11002-9 2.65
MUSG11003-10 2.05
MUSG11003-2 2.1
MUSG11004-5 3.2
MUSG11004-9 2.94
MUSG11006-15 2.3
MUSG11006-8 3.46
MUSG11007-1 2.2
MUSG11007-15 2.1
MUSG11008-12 3.1
MUSG11010-11 1.9
MUSG11010-19 2.55
MUSG11010-7 2.25
MUSG11011-3 2.05
MUSG11012-14 2.65
MUSG11016-10 4.1
MUSG11016-12 3
MUSG11016-14 2.55
MUSG11016-16 3.85
MUSG11016-18 2.05
MUSG11016-19 2.75
MUSG11016-2 3.25
MUSG11016-21 3.55
MUSG11016-22 3.23
MUSG11019-15 2.39
MUSG11019-17 2.65
MUSG11019-5 2.35
MUSG11021-16 2.6
MUSG11022-1 2.75
MUSG11022-10 1.95
MUSG11022-11 2.1
MUSG11023-11 3.1
MUSG11026-11 3.55
MUSG11030-9 2.9
MUSG11033-6 2.25
MUSG11036-3 2.1
MUSG11040-13 2.2
MUSG11040-15 2.11
MUSG11040-16 2.55
MUSG11042-7 2.43
MUSG11044-15 2.8
MUSG11044-16 3
MUSG11046-14 2.8
MUSG11046-18 2.3
MUSG11046-3 3.5
MUSG11046-7 2.65
MUSG11048-15 2.25
MUSG11048-16 2.1
MUSG11049-16 3.1
MUSG11049-2 1.9
MUSG11049-3 2.1
MUSG11049-5 2.35
MUSG11049-7 1.7
MUSG11050-3 2.2
Resisto 2.44

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Storage root damages estimatimg 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 237.441 4.0938 2.4487 0.000416731
REP 1 0.0338983 0.0338983 0.0202762 0.887262
Residuals 58 96.9661 1.67183 NA NA

The p-value for treatments is 0.000416731 which is significant at the 5% level.

The means of your treatments are:

germplasmName Storage root damages estimatimg 1-9
Chingova 6.5
Jonathan 1.5
MUSG11001-11 6
MUSG11001-2 4.5
MUSG11002-9 4
MUSG11003-10 4.5
MUSG11003-2 3
MUSG11004-5 5
MUSG11004-9 7.5
MUSG11006-15 5
MUSG11006-8 5
MUSG11007-1 5
MUSG11007-15 7
MUSG11008-12 7
MUSG11010-11 5
MUSG11010-19 4
MUSG11010-7 4
MUSG11011-3 2.5
MUSG11012-14 6
MUSG11016-10 3
MUSG11016-12 4.5
MUSG11016-14 4
MUSG11016-16 5
MUSG11016-18 4.5
MUSG11016-19 4.5
MUSG11016-2 4
MUSG11016-21 3.5
MUSG11016-22 4
MUSG11019-15 4.5
MUSG11019-17 4
MUSG11019-5 6.5
MUSG11021-16 6.5
MUSG11022-1 4
MUSG11022-10 4
MUSG11022-11 4
MUSG11023-11 3
MUSG11026-11 4.5
MUSG11030-9 4
MUSG11033-6 5
MUSG11036-3 2.5
MUSG11040-13 7
MUSG11040-15 5
MUSG11040-16 6.5
MUSG11042-7 4.5
MUSG11044-15 2.5
MUSG11044-16 2
MUSG11046-14 4.5
MUSG11046-18 6.5
MUSG11046-3 7.5
MUSG11046-7 4.5
MUSG11048-15 5
MUSG11048-16 6.5
MUSG11049-16 5
MUSG11049-2 5
MUSG11049-3 4.5
MUSG11049-5 6
MUSG11049-7 4.5
MUSG11050-3 4.5
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Storage root dry matter content computing percent

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 399154 6881.97 6.87707 3.92845e-12
REP 1 27.5156 27.5156 0.027496 0.868876
Residuals 58 58041.3 1000.71 NA NA

The p-value for treatments is 0.00000000000392845 which is significant at the 5% level.

The means of your treatments are:

germplasmName Storage root dry matter content computing percent
Chingova 306
Jonathan 435
MUSG11001-11 440
MUSG11001-2 429
MUSG11002-9 388
MUSG11003-10 389
MUSG11003-2 422
MUSG11004-5 451
MUSG11004-9 343
MUSG11006-15 433
MUSG11006-8 416
MUSG11007-1 423
MUSG11007-15 494
MUSG11008-12 381
MUSG11010-11 568
MUSG11010-19 422
MUSG11010-7 418
MUSG11011-3 326
MUSG11012-14 367
MUSG11016-10 320
MUSG11016-12 311
MUSG11016-14 335
MUSG11016-16 345
MUSG11016-18 338
MUSG11016-19 441
MUSG11016-2 332
MUSG11016-21 336
MUSG11016-22 357
MUSG11019-15 433
MUSG11019-17 358
MUSG11019-5 327
MUSG11021-16 451
MUSG11022-1 378
MUSG11022-10 435
MUSG11022-11 406
MUSG11023-11 354
MUSG11026-11 310
MUSG11030-9 314
MUSG11033-6 399
MUSG11036-3 472
MUSG11040-13 386
MUSG11040-15 386
MUSG11040-16 385
MUSG11042-7 399
MUSG11044-15 416
MUSG11044-16 401
MUSG11046-14 426
MUSG11046-18 407
MUSG11046-3 521
MUSG11046-7 400
MUSG11048-15 542
MUSG11048-16 394
MUSG11049-16 328
MUSG11049-2 344
MUSG11049-3 497
MUSG11049-5 407
MUSG11049-7 473
MUSG11050-3 365
Resisto 391

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Storage root form estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 153.864 2.65283 3.21687 8.0386e-06
REP 1 2.16949 2.16949 2.63076 0.110236
Residuals 58 47.8305 0.824664 NA NA

The p-value for treatments is 0.0000080386 which is significant at the 5% level.

The means of your treatments are:

germplasmName Storage root form estimating 1-9
Chingova 3
Jonathan 2
MUSG11001-11 5
MUSG11001-2 3
MUSG11002-9 3.5
MUSG11003-10 3
MUSG11003-2 4
MUSG11004-5 4
MUSG11004-9 4.5
MUSG11006-15 4
MUSG11006-8 5
MUSG11007-1 4.5
MUSG11007-15 4
MUSG11008-12 4
MUSG11010-11 3
MUSG11010-19 3.5
MUSG11010-7 5
MUSG11011-3 4.5
MUSG11012-14 5.5
MUSG11016-10 3
MUSG11016-12 3
MUSG11016-14 3
MUSG11016-16 4
MUSG11016-18 3
MUSG11016-19 5
MUSG11016-2 2
MUSG11016-21 4.5
MUSG11016-22 3
MUSG11019-15 5.5
MUSG11019-17 5
MUSG11019-5 5
MUSG11021-16 6
MUSG11022-1 3.5
MUSG11022-10 2.5
MUSG11022-11 5
MUSG11023-11 5
MUSG11026-11 3
MUSG11030-9 5
MUSG11033-6 4
MUSG11036-3 2.5
MUSG11040-13 5
MUSG11040-15 5
MUSG11040-16 5.5
MUSG11042-7 5
MUSG11044-15 3
MUSG11044-16 3
MUSG11046-14 3
MUSG11046-18 3
MUSG11046-3 7
MUSG11046-7 4
MUSG11048-15 5
MUSG11048-16 5
MUSG11049-16 4
MUSG11049-2 3
MUSG11049-3 4
MUSG11049-5 6
MUSG11049-7 4
MUSG11050-3 5
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Storage root size estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 156.78 2.7031 2.06105 0.00333208
REP 1 1.4322 1.4322 1.09202 0.300359
Residuals 58 76.0678 1.31151 NA NA

The p-value for treatments is 0.00333208 which is significant at the 5% level.

The means of your treatments are:

germplasmName Storage root size estimating 1-9
Chingova 2.5
Jonathan 1.5
MUSG11001-11 5
MUSG11001-2 3
MUSG11002-9 2.5
MUSG11003-10 4
MUSG11003-2 4
MUSG11004-5 2.5
MUSG11004-9 4
MUSG11006-15 3.5
MUSG11006-8 5.5
MUSG11007-1 4
MUSG11007-15 4
MUSG11008-12 4.5
MUSG11010-11 4
MUSG11010-19 2.5
MUSG11010-7 6
MUSG11011-3 2.5
MUSG11012-14 5
MUSG11016-10 3
MUSG11016-12 2
MUSG11016-14 2.5
MUSG11016-16 4
MUSG11016-18 2.5
MUSG11016-19 2.5
MUSG11016-2 2
MUSG11016-21 2.5
MUSG11016-22 2.5
MUSG11019-15 5
MUSG11019-17 5
MUSG11019-5 5
MUSG11021-16 5
MUSG11022-1 2
MUSG11022-10 3
MUSG11022-11 4
MUSG11023-11 4
MUSG11026-11 2.5
MUSG11030-9 3
MUSG11033-6 5
MUSG11036-3 3
MUSG11040-13 3
MUSG11040-15 5
MUSG11040-16 4
MUSG11042-7 5
MUSG11044-15 3
MUSG11044-16 2.5
MUSG11046-14 3.5
MUSG11046-18 4
MUSG11046-3 4
MUSG11046-7 4
MUSG11048-15 6
MUSG11048-16 5
MUSG11049-16 5
MUSG11049-2 2.5
MUSG11049-3 4
MUSG11049-5 4
MUSG11049-7 2.5
MUSG11050-3 4
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Storage root starch content measuring percent

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 4633.82 79.8935 4.57188 1.71783e-08
REP 1 3.01641 3.01641 0.172613 0.679333
Residuals 58 1013.55 17.475 NA NA

The p-value for treatments is 0.0000000171783 which is significant at the 5% level.

The means of your treatments are:

germplasmName Storage root starch content measuring percent
Chingova 61.7
Jonathan 50.4
MUSG11001-11 53.1
MUSG11001-2 53.4
MUSG11002-9 59.3
MUSG11003-10 60.2
MUSG11003-2 54.2
MUSG11004-5 52.9
MUSG11004-9 61.7
MUSG11006-15 52.7
MUSG11006-8 50.7
MUSG11007-1 55.6
MUSG11007-15 42.7
MUSG11008-12 49.2
MUSG11010-11 36.8
MUSG11010-19 50.3
MUSG11010-7 58.2
MUSG11011-3 60
MUSG11012-14 58.6
MUSG11016-10 63.4
MUSG11016-12 61.9
MUSG11016-14 59.5
MUSG11016-16 60.5
MUSG11016-18 60.3
MUSG11016-19 51
MUSG11016-2 59.3
MUSG11016-21 60.4
MUSG11016-22 56.5
MUSG11019-15 53
MUSG11019-17 52.5
MUSG11019-5 54.4
MUSG11021-16 51.1
MUSG11022-1 57.6
MUSG11022-10 44.5
MUSG11022-11 53.8
MUSG11023-11 56.3
MUSG11026-11 66.7
MUSG11030-9 62
MUSG11033-6 47
MUSG11036-3 53.1
MUSG11040-13 48.9
MUSG11040-15 58.9
MUSG11040-16 51.5
MUSG11042-7 57.4
MUSG11044-15 50.2
MUSG11044-16 50.4
MUSG11046-14 53
MUSG11046-18 48.6
MUSG11046-3 46.4
MUSG11046-7 56
MUSG11048-15 35.7
MUSG11048-16 48.7
MUSG11049-16 65.7
MUSG11049-2 63.6
MUSG11049-3 51.4
MUSG11049-5 47.6
MUSG11049-7 53.2
MUSG11050-3 55
Resisto 55.9

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Storage root sweetness 1 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 282.458 4.86996 1.80065 0.0134052
REP 1 0.135593 0.135593 0.0501351 0.823615
Residuals 58 156.864 2.70456 NA NA

The p-value for treatments is 0.0134052 which is significant at the 5% level.

The means of your treatments are:

germplasmName Storage root sweetness 1 estimating 1-9
Chingova 7
Jonathan 4
MUSG11001-11 7
MUSG11001-2 4
MUSG11002-9 5.5
MUSG11003-10 4.5
MUSG11003-2 8
MUSG11004-5 6.5
MUSG11004-9 7
MUSG11006-15 8
MUSG11006-8 8
MUSG11007-1 4
MUSG11007-15 7
MUSG11008-12 5
MUSG11010-11 8
MUSG11010-19 6
MUSG11010-7 7.5
MUSG11011-3 7
MUSG11012-14 8
MUSG11016-10 5
MUSG11016-12 4
MUSG11016-14 5
MUSG11016-16 5
MUSG11016-18 5
MUSG11016-19 3.5
MUSG11016-2 5.5
MUSG11016-21 4
MUSG11016-22 6
MUSG11019-15 6.5
MUSG11019-17 6
MUSG11019-5 4
MUSG11021-16 6.5
MUSG11022-1 7
MUSG11022-10 8
MUSG11022-11 6.5
MUSG11023-11 4
MUSG11026-11 4.5
MUSG11030-9 7
MUSG11033-6 8
MUSG11036-3 7
MUSG11040-13 7
MUSG11040-15 5
MUSG11040-16 7
MUSG11042-7 6
MUSG11044-15 6.5
MUSG11044-16 8
MUSG11046-14 5
MUSG11046-18 4
MUSG11046-3 7.5
MUSG11046-7 5
MUSG11048-15 9
MUSG11048-16 7
MUSG11049-16 4.5
MUSG11049-2 6
MUSG11049-3 5
MUSG11049-5 7
MUSG11049-7 5
MUSG11050-3 4.5
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Storage root texture 1 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 172.119 2.96756 1.89001 0.00833833
REP 1 1.4322 1.4322 0.912153 0.343508
Residuals 58 91.0678 1.57013 NA NA

The p-value for treatments is 0.00833833 which is significant at the 5% level.

The means of your treatments are:

germplasmName Storage root texture 1 estimating 1-9
Chingova 6
Jonathan 2
MUSG11001-11 3.5
MUSG11001-2 4
MUSG11002-9 3
MUSG11003-10 3
MUSG11003-2 4.5
MUSG11004-5 4.5
MUSG11004-9 3.5
MUSG11006-15 2.5
MUSG11006-8 3
MUSG11007-1 4.5
MUSG11007-15 2.5
MUSG11008-12 6
MUSG11010-11 4
MUSG11010-19 4
MUSG11010-7 3.5
MUSG11011-3 6
MUSG11012-14 4.5
MUSG11016-10 7
MUSG11016-12 5.5
MUSG11016-14 5
MUSG11016-16 7
MUSG11016-18 5
MUSG11016-19 4
MUSG11016-2 4
MUSG11016-21 4.5
MUSG11016-22 4.5
MUSG11019-15 3.5
MUSG11019-17 3.5
MUSG11019-5 6
MUSG11021-16 4
MUSG11022-1 5
MUSG11022-10 3.5
MUSG11022-11 4
MUSG11023-11 6
MUSG11026-11 6.5
MUSG11030-9 4
MUSG11033-6 4
MUSG11036-3 3
MUSG11040-13 4
MUSG11040-15 3.5
MUSG11040-16 6
MUSG11042-7 5
MUSG11044-15 4.5
MUSG11044-16 5
MUSG11046-14 3
MUSG11046-18 4.5
MUSG11046-3 4.5
MUSG11046-7 3
MUSG11048-15 4
MUSG11048-16 5
MUSG11049-16 5.5
MUSG11049-2 6
MUSG11049-3 4
MUSG11049-5 3
MUSG11049-7 4
MUSG11050-3 4
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Survival index computing percent

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 2.15113 0.0370885 2.1895 0.00166928
REP 1 0.175505 0.175505 10.3609 0.00210914
Residuals 58 0.982475 0.0169392 NA NA

The p-value for treatments is 0.00166928 which is significant at the 5% level.

The means of your treatments are:

germplasmName Survival index computing percent
Chingova 0.2
Jonathan 0.23
MUSG11001-11 0.4
MUSG11001-2 0.4
MUSG11002-9 0.35
MUSG11003-10 0.6
MUSG11003-2 0.25
MUSG11004-5 0.85
MUSG11004-9 0.25
MUSG11006-15 0.45
MUSG11006-8 0.35
MUSG11007-1 0.3
MUSG11007-15 0.4
MUSG11008-12 0.3
MUSG11010-11 0.4
MUSG11010-19 0.65
MUSG11010-7 0.3
MUSG11011-3 0.45
MUSG11012-14 0.4
MUSG11016-10 0.4
MUSG11016-12 0.6
MUSG11016-14 0.4
MUSG11016-16 0.6
MUSG11016-18 0.5
MUSG11016-19 0.45
MUSG11016-2 0.5
MUSG11016-21 0.5
MUSG11016-22 0.35
MUSG11019-15 0.45
MUSG11019-17 0.5
MUSG11019-5 0.4
MUSG11021-16 0.5
MUSG11022-1 0.35
MUSG11022-10 0.6
MUSG11022-11 0.5
MUSG11023-11 0.75
MUSG11026-11 0.35
MUSG11030-9 0.35
MUSG11033-6 0.55
MUSG11036-3 0.3
MUSG11040-13 0.55
MUSG11040-15 0.4
MUSG11040-16 0.55
MUSG11042-7 0.25
MUSG11044-15 0.45
MUSG11044-16 0.2
MUSG11046-14 0.4
MUSG11046-18 0.5
MUSG11046-3 0.65
MUSG11046-7 0.55
MUSG11048-15 0.45
MUSG11048-16 0.25
MUSG11049-16 0.5
MUSG11049-2 0.45
MUSG11049-3 0.55
MUSG11049-5 0.7
MUSG11049-7 0.45
MUSG11050-3 0.55
Resisto 0.367

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Sweet potato weevil symptoms 1 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 182.966 3.15459 1.37991 0.111553
REP 1 46.4068 46.4068 20.2996 3.25953e-05
Residuals 58 132.593 2.28609 NA NA

The means of your treatments are:

germplasmName Sweet potato weevil symptoms 1 estimating 1-9
Chingova 3
Jonathan 4
MUSG11001-11 6
MUSG11001-2 3.5
MUSG11002-9 4
MUSG11003-10 3.5
MUSG11003-2 2
MUSG11004-5 4.5
MUSG11004-9 5.5
MUSG11006-15 4
MUSG11006-8 5.5
MUSG11007-1 6
MUSG11007-15 4.5
MUSG11008-12 6
MUSG11010-11 3
MUSG11010-19 3
MUSG11010-7 3.5
MUSG11011-3 4.5
MUSG11012-14 4
MUSG11016-10 3
MUSG11016-12 5
MUSG11016-14 2.5
MUSG11016-16 3.5
MUSG11016-18 4.5
MUSG11016-19 4
MUSG11016-2 3
MUSG11016-21 2.5
MUSG11016-22 7
MUSG11019-15 2.5
MUSG11019-17 6.5
MUSG11019-5 3.5
MUSG11021-16 6
MUSG11022-1 4
MUSG11022-10 2.5
MUSG11022-11 3
MUSG11023-11 5.5
MUSG11026-11 4
MUSG11030-9 4
MUSG11033-6 2.5
MUSG11036-3 3
MUSG11040-13 4
MUSG11040-15 2.5
MUSG11040-16 5.5
MUSG11042-7 3
MUSG11044-15 4.5
MUSG11044-16 5
MUSG11046-14 3.5
MUSG11046-18 2.5
MUSG11046-3 4
MUSG11046-7 4
MUSG11048-15 5.5
MUSG11048-16 4
MUSG11049-16 5.5
MUSG11049-2 2
MUSG11049-3 4
MUSG11049-5 4
MUSG11049-7 3.5
MUSG11050-3 5
Resisto 1

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Total carotenoids measuring mg per 100g

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 2261.3 38.9879 7.26569 1.16682e-12
REP 1 9.22229 9.22229 1.71865 0.195034
Residuals 58 311.229 5.36602 NA NA

The p-value for treatments is 0.00000000000116682 which is significant at the 5% level.

The means of your treatments are:

germplasmName Total carotenoids measuring mg per 100g
Chingova 0.02
Jonathan 9.27
MUSG11001-11 12.4
MUSG11001-2 13.4
MUSG11002-9 14.4
MUSG11003-10 7.23
MUSG11003-2 11
MUSG11004-5 14.4
MUSG11004-9 7.23
MUSG11006-15 4.47
MUSG11006-8 14.4
MUSG11007-1 14.4
MUSG11007-15 10.8
MUSG11008-12 3.03
MUSG11010-11 14.4
MUSG11010-19 12.5
MUSG11010-7 14.4
MUSG11011-3 1.38
MUSG11012-14 6.12
MUSG11016-10 4.6
MUSG11016-12 0.02
MUSG11016-14 11
MUSG11016-16 1.74
MUSG11016-18 8.05
MUSG11016-19 13.4
MUSG11016-2 1.74
MUSG11016-21 14.4
MUSG11016-22 6.12
MUSG11019-15 14.4
MUSG11019-17 12.4
MUSG11019-5 4.38
MUSG11021-16 11.7
MUSG11022-1 12.4
MUSG11022-10 14.4
MUSG11022-11 11.4
MUSG11023-11 8.06
MUSG11026-11 5.49
MUSG11030-9 6.12
MUSG11033-6 12.4
MUSG11036-3 14.4
MUSG11040-13 9.26
MUSG11040-15 12.4
MUSG11040-16 12.4
MUSG11042-7 14.4
MUSG11044-15 13.4
MUSG11044-16 14.4
MUSG11046-14 14.4
MUSG11046-18 6.12
MUSG11046-3 14.4
MUSG11046-7 5.46
MUSG11048-15 14.4
MUSG11048-16 13.4
MUSG11049-16 11
MUSG11049-2 4.25
MUSG11049-3 11
MUSG11049-5 13.4
MUSG11049-7 7.23
MUSG11050-3 14.4
Resisto 11.2

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Vine vigor 1 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 207.153 3.5716 3.37998 3.62432e-06
REP 1 34.7119 34.7119 32.8496 3.76116e-07
Residuals 58 61.2881 1.05669 NA NA

The p-value for treatments is 0.00000362432 which is significant at the 5% level.

The means of your treatments are:

germplasmName Vine vigor 1 estimating 1-9
Chingova 4.5
Jonathan 3.5
MUSG11001-11 6.5
MUSG11001-2 4
MUSG11002-9 5.5
MUSG11003-10 7
MUSG11003-2 5.5
MUSG11004-5 8.5
MUSG11004-9 5
MUSG11006-15 7.5
MUSG11006-8 5
MUSG11007-1 4.5
MUSG11007-15 7
MUSG11008-12 5.5
MUSG11010-11 5.5
MUSG11010-19 7
MUSG11010-7 4.5
MUSG11011-3 6.5
MUSG11012-14 5
MUSG11016-10 6.5
MUSG11016-12 7
MUSG11016-14 6.5
MUSG11016-16 6
MUSG11016-18 3
MUSG11016-19 8
MUSG11016-2 5.5
MUSG11016-21 6
MUSG11016-22 5.5
MUSG11019-15 7
MUSG11019-17 7
MUSG11019-5 6
MUSG11021-16 6.5
MUSG11022-1 5
MUSG11022-10 5.5
MUSG11022-11 7.5
MUSG11023-11 9
MUSG11026-11 6
MUSG11030-9 4.5
MUSG11033-6 5
MUSG11036-3 5.5
MUSG11040-13 8
MUSG11040-15 6.5
MUSG11040-16 7
MUSG11042-7 5.5
MUSG11044-15 6.5
MUSG11044-16 5
MUSG11046-14 7.5
MUSG11046-18 5.5
MUSG11046-3 7
MUSG11046-7 6.5
MUSG11048-15 6
MUSG11048-16 5.5
MUSG11049-16 6.5
MUSG11049-2 6
MUSG11049-3 8
MUSG11049-5 8.5
MUSG11049-7 8
MUSG11050-3 6.5
Resisto 2.5

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Virus symptoms 1 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 32.7627 0.564874 1.58313 0.0414329
REP 1 0.305085 0.305085 0.855037 0.358963
Residuals 58 20.6949 0.356809 NA NA

The p-value for treatments is 0.0414329 which is significant at the 5% level.

The means of your treatments are:

germplasmName Virus symptoms 1 estimating 1-9
Chingova 1
Jonathan 2
MUSG11001-11 1
MUSG11001-2 1.5
MUSG11002-9 1
MUSG11003-10 1
MUSG11003-2 1
MUSG11004-5 1
MUSG11004-9 3.5
MUSG11006-15 1
MUSG11006-8 1.5
MUSG11007-1 1.5
MUSG11007-15 1
MUSG11008-12 1
MUSG11010-11 1.5
MUSG11010-19 1
MUSG11010-7 1.5
MUSG11011-3 1
MUSG11012-14 1.5
MUSG11016-10 2
MUSG11016-12 1.5
MUSG11016-14 1.5
MUSG11016-16 1.5
MUSG11016-18 1
MUSG11016-19 2
MUSG11016-2 2.5
MUSG11016-21 1
MUSG11016-22 1.5
MUSG11019-15 1
MUSG11019-17 1
MUSG11019-5 1.5
MUSG11021-16 1
MUSG11022-1 1
MUSG11022-10 1
MUSG11022-11 1
MUSG11023-11 1.5
MUSG11026-11 1
MUSG11030-9 1
MUSG11033-6 1
MUSG11036-3 1
MUSG11040-13 1.5
MUSG11040-15 1
MUSG11040-16 1
MUSG11042-7 1.5
MUSG11044-15 1
MUSG11044-16 2.5
MUSG11046-14 1
MUSG11046-18 3
MUSG11046-3 1
MUSG11046-7 2
MUSG11048-15 1
MUSG11048-16 1
MUSG11049-16 1
MUSG11049-2 1
MUSG11049-3 1
MUSG11049-5 1
MUSG11049-7 1
MUSG11050-3 1
Resisto 1.5

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Virus symptoms 2 estimating 1-9

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 44.7797 0.772063 2.66331 0.000134289
REP 1 0.686441 0.686441 2.36794 0.129288
Residuals 58 16.8136 0.289889 NA NA

The p-value for treatments is 0.000134289 which is significant at the 5% level.

The means of your treatments are:

germplasmName Virus symptoms 2 estimating 1-9
Chingova 1
Jonathan 3
MUSG11001-11 1
MUSG11001-2 1.5
MUSG11002-9 1
MUSG11003-10 1
MUSG11003-2 1
MUSG11004-5 1
MUSG11004-9 3.5
MUSG11006-15 1.5
MUSG11006-8 1.5
MUSG11007-1 1
MUSG11007-15 2
MUSG11008-12 1
MUSG11010-11 1.5
MUSG11010-19 1
MUSG11010-7 1.5
MUSG11011-3 1
MUSG11012-14 1.5
MUSG11016-10 2
MUSG11016-12 1.5
MUSG11016-14 2
MUSG11016-16 1
MUSG11016-18 1.5
MUSG11016-19 1.5
MUSG11016-2 3.5
MUSG11016-21 1
MUSG11016-22 1.5
MUSG11019-15 1
MUSG11019-17 1
MUSG11019-5 1
MUSG11021-16 1
MUSG11022-1 1
MUSG11022-10 1
MUSG11022-11 1
MUSG11023-11 1.5
MUSG11026-11 1
MUSG11030-9 1
MUSG11033-6 1
MUSG11036-3 1
MUSG11040-13 1
MUSG11040-15 1
MUSG11040-16 1
MUSG11042-7 2
MUSG11044-15 1.5
MUSG11044-16 2.5
MUSG11046-14 1.5
MUSG11046-18 3
MUSG11046-3 1.5
MUSG11046-7 2
MUSG11048-15 1
MUSG11048-16 1
MUSG11049-16 1
MUSG11049-2 1.5
MUSG11049-3 1
MUSG11049-5 1
MUSG11049-7 1
MUSG11050-3 1
Resisto 1.5

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Weight of commercial storage roots measuring kg per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 3022.22 52.1072 2.94443 3.14744e-05
REP 1 1132.35 1132.35 63.9861 6.14524e-11
Residuals 58 1026.42 17.6969 NA NA

The p-value for treatments is 0.0000314744 which is significant at the 5% level.

The means of your treatments are:

germplasmName Weight of commercial storage roots measuring kg per plot
Chingova 0.75
Jonathan 7.62
MUSG11001-11 8.5
MUSG11001-2 5.7
MUSG11002-9 15.5
MUSG11003-10 16.1
MUSG11003-2 6.1
MUSG11004-5 29
MUSG11004-9 3.05
MUSG11006-15 11.7
MUSG11006-8 0.55
MUSG11007-1 2.95
MUSG11007-15 11.6
MUSG11008-12 5
MUSG11010-11 6.5
MUSG11010-19 15.5
MUSG11010-7 2.45
MUSG11011-3 8.5
MUSG11012-14 4.5
MUSG11016-10 12.9
MUSG11016-12 15
MUSG11016-14 11.5
MUSG11016-16 12
MUSG11016-18 11.1
MUSG11016-19 7.95
MUSG11016-2 15.5
MUSG11016-21 9.5
MUSG11016-22 10.5
MUSG11019-15 9
MUSG11019-17 3.75
MUSG11019-5 3.95
MUSG11021-16 10.1
MUSG11022-1 10
MUSG11022-10 8.45
MUSG11022-11 8.75
MUSG11023-11 17
MUSG11026-11 14.5
MUSG11030-9 6.5
MUSG11033-6 7.65
MUSG11036-3 8.1
MUSG11040-13 11
MUSG11040-15 4.1
MUSG11040-16 10.6
MUSG11042-7 7.5
MUSG11044-15 3.55
MUSG11044-16 3.5
MUSG11046-14 7.05
MUSG11046-18 11.6
MUSG11046-3 14.5
MUSG11046-7 15.1
MUSG11048-15 5.1
MUSG11048-16 8.5
MUSG11049-16 5.2
MUSG11049-2 13.9
MUSG11049-3 19.5
MUSG11049-5 12.9
MUSG11049-7 11.2
MUSG11050-3 14
Resisto 7.19

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Weight of non-commercial storage roots measuring kg per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 372.865 6.42871 2.61305 0.000174766
REP 1 35.7325 35.7325 14.5241 0.000337047
Residuals 58 142.693 2.46023 NA NA

The p-value for treatments is 0.000174766 which is significant at the 5% level.

The means of your treatments are:

germplasmName Weight of non-commercial storage roots measuring kg per plot
Chingova 0.5
Jonathan 1.47
MUSG11001-11 4.25
MUSG11001-2 1.7
MUSG11002-9 2.5
MUSG11003-10 3.15
MUSG11003-2 0.7
MUSG11004-5 5.6
MUSG11004-9 1.1
MUSG11006-15 5.15
MUSG11006-8 1.65
MUSG11007-1 2
MUSG11007-15 2.5
MUSG11008-12 5
MUSG11010-11 2.3
MUSG11010-19 6
MUSG11010-7 1.55
MUSG11011-3 1.6
MUSG11012-14 3.1
MUSG11016-10 1.6
MUSG11016-12 4.65
MUSG11016-14 1.5
MUSG11016-16 4.2
MUSG11016-18 2.1
MUSG11016-19 3.5
MUSG11016-2 3.5
MUSG11016-21 2.15
MUSG11016-22 2.05
MUSG11019-15 3.2
MUSG11019-17 3.55
MUSG11019-5 0.45
MUSG11021-16 4.45
MUSG11022-1 1.6
MUSG11022-10 3.4
MUSG11022-11 3.1
MUSG11023-11 10
MUSG11026-11 1.6
MUSG11030-9 2.7
MUSG11033-6 4
MUSG11036-3 1.3
MUSG11040-13 5.5
MUSG11040-15 0.95
MUSG11040-16 5.55
MUSG11042-7 0.4
MUSG11044-15 2.78
MUSG11044-16 1.5
MUSG11046-14 1.55
MUSG11046-18 2.6
MUSG11046-3 6.2
MUSG11046-7 3.5
MUSG11048-15 1.95
MUSG11048-16 1.75
MUSG11049-16 3.15
MUSG11049-2 1.5
MUSG11049-3 2.9
MUSG11049-5 5.55
MUSG11049-7 5
MUSG11050-3 4
Resisto 2.04

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Analysis of Weight of vines measuring kg per plot

You have fitted a linear model for a RCBD. The ANOVA table for your model is:

  Df Sum Sq Mean Sq F value Pr(>F)
germplasmName 58 4489.13 77.3989 2.60186 0.000185357
REP 1 1716.73 1716.73 57.7101 2.91112e-10
Residuals 58 1725.36 29.7476 NA NA

The p-value for treatments is 0.000185357 which is significant at the 5% level.

The means of your treatments are:

germplasmName Weight of vines measuring kg per plot
Chingova 2
Jonathan 8.88
MUSG11001-11 16.8
MUSG11001-2 0.5
MUSG11002-9 11.2
MUSG11003-10 19.4
MUSG11003-2 3.8
MUSG11004-5 26.6
MUSG11004-9 2.05
MUSG11006-15 12.2
MUSG11006-8 11.1
MUSG11007-1 3.65
MUSG11007-15 10.2
MUSG11008-12 7.3
MUSG11010-11 11.3
MUSG11010-19 24.9
MUSG11010-7 4.25
MUSG11011-3 15.1
MUSG11012-14 10.1
MUSG11016-10 13.6
MUSG11016-12 20.2
MUSG11016-14 18.5
MUSG11016-16 9.2
MUSG11016-18 16.1
MUSG11016-19 21.9
MUSG11016-2 9.45
MUSG11016-21 8.59
MUSG11016-22 2.15
MUSG11019-15 13.3
MUSG11019-17 14.6
MUSG11019-5 9.05
MUSG11021-16 10.7
MUSG11022-1 5.15
MUSG11022-10 3.45
MUSG11022-11 18.6
MUSG11023-11 24.1
MUSG11026-11 8.3
MUSG11030-9 7.75
MUSG11033-6 6.75
MUSG11036-3 7.6
MUSG11040-13 27.1
MUSG11040-15 8.95
MUSG11040-16 15.7
MUSG11042-7 9.15
MUSG11044-15 11.3
MUSG11044-16 7.2
MUSG11046-14 17.8
MUSG11046-18 7.8
MUSG11046-3 14.7
MUSG11046-7 17.4
MUSG11048-15 10.1
MUSG11048-16 11.8
MUSG11049-16 13.2
MUSG11049-2 10.2
MUSG11049-3 16.1
MUSG11049-5 15.8
MUSG11049-7 13.3
MUSG11050-3 11.1
Resisto 5.2

Do not forget the assumptions of the model. It is supposed that the error has a normal distribution with the same variance for all the treatments. The following plots must help you evaluate this:

Funnel shapes for the first plot may suggest heterogeneity of variances while departures from the theoretical normal line are symptoms of lack of normality.

Trait correlations

Variety candidate selection

Summary

References

Data sources

Literature